To find strong discriminative regions to enhance the fine-grained visual classification, the proposed approaches can be classified into three categories. The first type of method finds regions through Region Proposal Network (RPN)[1]}, such as NTS-Net[2]}, FDL[3]}, and StackedLSTM[4]}. The second type of method strengthens the feature map through the attention mechanism, such as CAL[5]}, MA-CNN[6]}, MAMC[7]}, API-Net[8]}, and WS-DAN[9]}. The third type of method uses the strength of the attention-map in the self-attention mechanism as the judgment of discriminative regions, such as TransFG[10]} and FFVT[11]}. The first two approaches are mainly based on convolutional neural networks (CNN), such as ResNet[12]}, DenseNet[13]}, and EfficientNet[14]}, while the third is based on ViT to implement fine-grained visual classification.
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